Legal claims defining the scope of protection. Each claim is shown in both the original legal language and a plain English translation.
1. A method in a cloud infrastructure of multi-level threshold service level agreement (SLA) violation mitigation, the method comprising: generating a model for an engineered feature (eF), wherein the eF is based on collected metrics; determining a first threshold T 1 , a second threshold T 2 , and a maximum threshold Tε for the eF based on the generated model of the eF, wherein the maximum threshold Tε when exceeded indicates that a SLA violation occurred, and wherein the first threshold T 1 and the second threshold T 2 are intermediate thresholds that are smaller than the maximum threshold Tε; estimating at a predetermined frequency a value of the eF based on real-time metrics; responsive to determining that the value of the eF exceeds Tε, adjusting the values of T 1 and T 2 and modifying the predetermined frequency and sending to a cloud orchestrator a message indicating that an SLA violation of type Tε has occurred; responsive to determining that the value of the eF does not exceed Tε, determining whether the value of eF exceeds T 1 ; responsive to determining that the value of eF exceeds T 1 , determining whether a change in rate of eF (eF′) has been positive; responsive to determining that the change in rate of eF has been positive, determining whether the value of eF exceeds T 2 ; responsive to determining that the value of eF exceeds T 2 , determining whether a change in the rate of eF′ (eF″) exceeds a value 1 ; responsive to determining that the value of eF″ exceeds the value 1 , triggering a SLA violation warning of type value 1 ; responsive to determining that the value of eF does not exceed T 2 , determining whether the change in the rate of eF″ exceeds a value 2 ; responsive to determining that the value of eF″ exceeds the value 2 , triggering a SLA violation warning of type value 2 ; determining one or more elements of the eF that triggered the SLA violation warning; and sending to the cloud orchestrator a message indicating the one or more elements.
A method for preventing service level agreement (SLA) violations in a cloud infrastructure uses multiple threshold levels for an "engineered feature" (eF) derived from collected metrics. First, a model is created for the eF. Then, three thresholds are defined: T1, T2 (intermediate thresholds), and Tε (maximum threshold). The eF's value is estimated regularly. If the eF exceeds Tε, it indicates an SLA violation; T1 and T2 are adjusted, the estimation frequency is changed, and a violation message is sent to a cloud orchestrator. If eF doesn't exceed Tε, the method checks if it exceeds T1. If it does, it checks if the rate of change of eF (eF') is positive. If so, it checks if eF exceeds T2. If it does, it checks if the rate of change of eF' (eF'') exceeds 1, triggering an SLA warning of type "value 1". If eF doesn't exceed T2, it checks if eF'' exceeds 2, triggering an SLA warning of type "value 2". Finally, the elements of eF that triggered the warning are identified and sent to the cloud orchestrator.
2. The method of claim 1 , wherein the triggering of a SLA violation warning includes sending to the cloud orchestrator a message indicating that a SLA violation is likely to occur soon.
The method described for preventing SLA violations in a cloud infrastructure includes generating a model for an engineered feature (eF), determining thresholds T1, T2, and Tε, estimating the eF value, and sending a message to a cloud orchestrator if Tε is exceeded (as in Claim 1). The method extends to sending a message to the cloud orchestrator that a SLA violation is likely to occur soon when a SLA violation warning is triggered. This warning alerts the orchestrator to a potential issue before it escalates into a full SLA breach, allowing for proactive mitigation.
3. The method of claim 2 , wherein the message further includes the type of the SLA violation warning, and wherein the type of the violation warning is one of the type of value 1 and the type of value 2 .
The method described for preventing SLA violations in a cloud infrastructure includes generating a model for an engineered feature (eF), determining thresholds T1, T2, and Tε, estimating the eF value, and sending a message to a cloud orchestrator if Tε is exceeded (as in Claim 1). The extension from Claim 2 sends a message that a SLA violation is likely to occur soon. The warning message also includes the type of potential SLA violation, such as "value 1" or "value 2", providing more specific context to the orchestrator regarding the nature of the impending issue.
4. The method of claim 1 , wherein the determining the one or more elements of the eF that triggered the SLA violation warning further comprises: selecting a predetermined number of raw input metrics for the eF that have the highest rate of change; and selecting a set of raw input metrics used to determine the eF that have exceeded their respective rate of change or rate of rate of change thresholds.
The method described for preventing SLA violations in a cloud infrastructure includes generating a model for an engineered feature (eF), determining thresholds T1, T2, and Tε, estimating the eF value, and sending a message to a cloud orchestrator if Tε is exceeded (as in Claim 1). The method identifies the elements of eF triggering SLA violation warnings by selecting a predetermined number of raw input metrics with the highest rate of change and also selecting metrics that have exceeded their respective rate of change thresholds. This helps pinpoint the root cause of the potential violation.
5. The method of claim 1 , wherein the determining the one or more elements of the eF that triggered the SLA violation warning further comprises: selecting a cluster from a K-means analysis of SLA violation warnings of the eF that the SLA violation warning belongs to, where the K-means analysis of the SLA violation warning is based on collected raw input metrics and eF values of prior SLA violation warnings for that eF.
The method described for preventing SLA violations in a cloud infrastructure includes generating a model for an engineered feature (eF), determining thresholds T1, T2, and Tε, estimating the eF value, and sending a message to a cloud orchestrator if Tε is exceeded (as in Claim 1). The method identifies the elements of eF triggering SLA violation warnings by using K-means clustering analysis on past SLA violation warnings. The current warning is assigned to a cluster based on its raw input metrics and eF values. The cluster represents similar past violations, helping to identify likely problematic elements.
6. The method of claim 1 , wherein the estimating the value of the eF comprises determining the value of eF using one of a parallel select machine learning model, a parallel-combine machine learning model, and a sequential machine learning model.
The method described for preventing SLA violations in a cloud infrastructure includes generating a model for an engineered feature (eF), determining thresholds T1, T2, and Tε, estimating the eF value, and sending a message to a cloud orchestrator if Tε is exceeded (as in Claim 1). The estimation of the eF value involves using either a parallel select, a parallel-combine, or a sequential machine learning model. This allows flexibility in choosing the most appropriate model based on the nature of the eF and available resources.
7. The method of claim 1 , wherein the generating the model of the eF comprises: estimating one or more eF values based on historically collected metrics; and creating a model based on the one or more eF values.
The method described for preventing SLA violations in a cloud infrastructure includes generating a model for an engineered feature (eF), determining thresholds T1, T2, and Tε, estimating the eF value, and sending a message to a cloud orchestrator if Tε is exceeded (as in Claim 1). The eF model is created by first estimating eF values based on historical metrics and then building a model based on those estimated values. This provides a baseline for predicting future eF behavior.
8. The method of claim 1 , wherein the determining the T 1 , T 2 , and the Tε further comprise: determining the T 1 , T 2 , and the Tε based on at least one of a set of predetermined values, a domain expert, and analysis of past performance.
The method described for preventing SLA violations in a cloud infrastructure includes generating a model for an engineered feature (eF), determining thresholds T1, T2, and Tε, estimating the eF value, and sending a message to a cloud orchestrator if Tε is exceeded (as in Claim 1). The thresholds T1, T2, and Tε are determined based on either predetermined values, input from a domain expert, analysis of past performance, or a combination of these factors. This allows for flexible threshold setting based on available data and knowledge.
9. The method of claim 1 , further comprising: responsive to determining that the value of eF exceeds T 2 , updating a counter value; and responsive to determining that the counter value exceeds a value x, triggering a SLA violation warning of type value x and resetting the counter value.
The method described for preventing SLA violations in a cloud infrastructure includes generating a model for an engineered feature (eF), determining thresholds T1, T2, and Tε, estimating the eF value, and sending a message to a cloud orchestrator if Tε is exceeded (as in Claim 1). Additionally, if the eF exceeds T2, a counter is incremented. If the counter exceeds a value "x", an SLA violation warning of type "value x" is triggered and the counter is reset. This allows the system to detect persistent or repeated threshold breaches, even if they don't immediately trigger a maximum threshold violation.
10. A non-transitory computer readable medium, having stored thereon a computer program, which when executed by a processor performs the following operations: generating a model for an engineered feature (eF), wherein the eF is based on collected metrics; determining a first threshold T 1 , a second threshold T 2 , and a maximum threshold Tε for the eF based on the generated model of the eF, wherein the maximum threshold Tε if exceeded by a value of the eF indicates that a SLA violation occurred, and wherein the first threshold T 1 and the second threshold T 2 are intermediate thresholds that are smaller than the maximum threshold Tε; estimating at a predetermined frequency a value of the eF based on real-time metrics; responsive to determining that the value of the eF exceeds Tε, adjusting the values of T 1 and T 2 and modifying the predetermined frequency and sending to a cloud orchestrator a message indicating that an SLA violation of type Tε has occurred; responsive to determining that the value of the eF does not exceed Tε, determining whether the value of eF exceeds T 1 ; responsive to determining that the value of eF exceeds T 1 , determining whether a change in rate of eF (eF′) has been positive; responsive to determining that the change in rate of eF has been positive, determining whether the value of eF exceeds T 2 ; responsive to determining that the value of eF exceeds T 2 , determining whether a change in the rate of eF′ (eF″) exceeds a value 1 ; responsive to determining that the value of eF″ exceeds the value 1 , triggering a SLA violation warning of type value 1 ; responsive to determining that the value of eF does not exceed T 2 , determining whether the change in the rate of eF″ exceeds a value 2 ; responsive to determining that the value of eF″ exceeds the value 2 , triggering a SLA violation warning of type value 2 ; determining one or more elements of the eF that triggered the SLA violation warning; and sending to the cloud orchestrator a message indicating the one or more elements.
A non-transitory computer-readable medium stores a program for multi-level threshold SLA violation mitigation in a cloud infrastructure. The program generates a model for an engineered feature (eF) based on metrics, determines thresholds T1, T2, and Tε, and estimates the eF's value. If the eF exceeds Tε, T1 and T2 are adjusted, the estimation frequency is changed, and a violation message is sent. If eF doesn't exceed Tε, checks are performed to determine if the value of eF exceeds T1; if the rate of change of eF (eF') is positive; if eF exceeds T2; if the rate of change of eF' (eF'') exceeds 1 or 2, triggering corresponding SLA violation warnings. The elements triggering the warnings are identified and sent to a cloud orchestrator.
11. The non-transitory computer readable medium claim 10 , wherein the triggering of a SLA violation warning includes sending to the cloud orchestrator a message indicating that a SLA violation is likely to occur soon.
The non-transitory computer-readable medium, as described in Claim 10, stores a program that, upon triggering a SLA violation warning (based on exceeding calculated thresholds T1, T2, Tε for an engineered feature), sends a message to the cloud orchestrator indicating that a SLA violation is likely to occur soon. This early warning system alerts the orchestrator to a potential issue before it escalates.
12. The non-transitory computer readable medium of claim 10 , wherein the message further includes the type of the SLA violation warning, and wherein the type of the violation warning is one of the type of value 1 and the type of value 2 .
The non-transitory computer-readable medium, as described in Claim 10, stores a program that, upon triggering a SLA violation warning (based on exceeding calculated thresholds T1, T2, Tε for an engineered feature, indicating potential values 1 or 2 as type of the violation), sends a message, also including the specific type of the impending violation ("value 1" or "value 2") to the cloud orchestrator for more targeted response.
13. The non-transitory computer readable medium of claim 10 , wherein the determining the one or more elements of the eF that triggered the SLA violation warning further comprises: selecting a predetermined number of raw input metrics for the eF that have the highest rate of change; and selecting a set of raw input metrics used to determine the eF that have exceeded their respective rate of change or rate of rate of change thresholds.
The non-transitory computer-readable medium, as described in Claim 10, stores a program that identifies the triggering elements of the engineered feature (eF) that led to a SLA violation warning (based on exceeding calculated thresholds T1, T2, Tε). It selects a predetermined number of raw input metrics for the eF that have the highest rate of change, and also selects raw input metrics that have exceeded their rate of change or rate of rate of change thresholds to provide details regarding the cause of violation.
14. The non-transitory computer readable medium of claim 10 , wherein the determining the one or more elements of the eF that triggered the SLA violation warning further comprises: selecting a cluster from a K-means analysis of SLA violations warnings of the eF that the SLA violation warning belongs to, where the K-means analysis of the SLA violation warning is based on collected raw input metrics and eF values of prior SLA violation warnings for that eF.
The non-transitory computer-readable medium, as described in Claim 10, stores a program that identifies the triggering elements of the engineered feature (eF) that led to a SLA violation warning (based on exceeding calculated thresholds T1, T2, Tε). It performs K-means clustering analysis on past SLA violation warnings. The program then selects the cluster of previous SLA violation warnings that is most similar, and uses its data of raw metrics and eF values of these similar prior warnings to understand the current violation.
15. The non-transitory computer readable medium of claim 10 , wherein the estimating the value of the eF comprises determining the value of eF using one of a parallel select machine learning model, a parallel-combine machine learning model, and a sequential machine learning model.
The non-transitory computer-readable medium, as described in Claim 10, stores a program that estimates the value of an engineered feature (eF), utilizing either a parallel select machine learning model, a parallel-combine machine learning model, or a sequential machine learning model. This estimation is crucial for determining whether thresholds have been exceeded and therefore can use flexible choices of ML model.
16. The non-transitory computer readable medium of claim 10 , wherein the generating the model of the eF comprises: estimating one or more eF values based on historically collected metrics; and creating a model based on the one or more eF values.
The non-transitory computer-readable medium, as described in Claim 10, stores a program that generates a model of an engineered feature (eF) by first estimating eF values based on historically collected metrics, and then creating a model based on those estimated values. This model serves as a predictive tool to monitor and forecast potential SLA violations.
17. The non-transitory computer readable medium of claim 10 , wherein the determining the T 1 , T 2 , and the Tε further comprise: determining the T 1 , T 2 , and the Tε based on at least one of a set of predetermined values, a domain expert, and analysis of past performance.
The non-transitory computer-readable medium, as described in Claim 10, stores a program that determines the thresholds T1, T2, and Tε based on at least one of the following: a set of predetermined values, input from a domain expert, or analysis of past performance data. This flexible approach allows for customizing thresholds based on specific system requirements and historical trends.
18. The non-transitory computer readable medium of claim 10 , wherein the operations further comprise: responsive to determining that the value of eF exceeds T 2 , updating a counter value; and responsive to determining that the counter value exceeds a value x, triggering a SLA violation warning of type value x and resetting the counter value.
The non-transitory computer-readable medium, as described in Claim 10, stores a program that, if the value of the engineered feature (eF) exceeds T2, updates a counter value. If this counter value then exceeds a threshold "x", an SLA violation warning of type "value x" is triggered, and the counter value is reset. This provides an additional layer of protection by detecting and reacting to repeated threshold breaches.
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December 12, 2017
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